import torch
import torchvision
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

from src.model.train_gpu.model import Model



'''
模型训练步骤：
1. 准备数据集：测试以及训练的数据集
2. 加载数据集
3. 创建网络模型
4. 设置损失函数
5. 准备优化器
6. 开始训练：优化器优化模型，进行测试训练
'''

# 准备数据集
train_data = torchvision.datasets.CIFAR10(root='../../../data',train=True,transform=torchvision.transforms.ToTensor(),download=True)
test_data = torchvision.datasets.CIFAR10(root='../../../data',train=False,transform=torchvision.transforms.ToTensor(),download=True)

# 创建数据加载器
train_dataLoader = DataLoader(train_data,batch_size=64)
test_dataLoader = DataLoader(test_data,batch_size=64)

# 创建网络模型
model = Model()

# 设置损失函数
loss_fn = CrossEntropyLoss()

# 准备优化器
learn_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(),lr=learn_rate)

# 设置训练参数
# 训练次数和测试次数
total_train_step= 0
total_test_step= 0
# 训练轮数
epoch = 10
write = SummaryWriter('../../../logs_train')
# 训练
for i in range(epoch):
    print("-----第{}轮训练开始-----".format(i+1))
    model.train()
    for data in train_dataLoader:
        imgs,targets = data
        outputs = model(imgs)
        loss = loss_fn(outputs,targets)

        # 优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step += 1
        if total_train_step % 100 == 0:
            print(("训练次数：{},loss:{}".format(total_train_step,loss.item())))
            write.add_scalar("train_loss",loss.item(),total_train_step)

    # 测试
    model.eval()
    total_test_loss = 0
    total_accuracy = 0
    with torch.no_grad():
        for data in test_dataLoader:
            imgs,targets = data
            outputs = model(imgs)
            loss = loss_fn(outputs,targets)
            total_test_loss += loss.item()
            total_test_step += 1
            accuracy = (outputs.argmax(1) == targets).sum()
            total_accuracy += accuracy
    print("整体测试集上的Loss:{}".format(total_test_loss))
    test_accuracy = total_accuracy/len(test_dataLoader)
    print("整体测试集上的正确率:{}".format(test_accuracy))
    write.add_scalar("test_loss",total_test_loss,total_test_step)
    write.add_scalar("test_accuracy",test_accuracy,total_test_step)
    total_test_step += 1
    torch.save(model,"model_{}.pth".format(i))
    print("模型已保存")
write.close()


